Multiple-Input Multiple-Output (MIMO) technology is ready for deployment in the near future. A variety of MIMO antenna types including cross-polarized antennas, uniform linear arrays, and remote radio heads will be available. While theoretical MIMO performance gains have thoroughly been investigated, one of the major tasks of network operators, the selection process of an optimal MIMO antenna type for every sector in cellular network planning and optimization workflows, has rarely been treated so far.
MIMO technology is specified by the 3GPP in Release 7 for HSPA+ and with even more options in Release 8 for LTE. Currently, network operators strongly ask for solutions that embed MIMO technology into the process of planning and optimization of cellular network deployments.
Planning and optimization of radio access networks of cellular radio networks comprises to define the configuration of the radio access network in terms of the number of base stations in a coverage area, the number of sectors per base station, the position of the base stations, the type of the antennas per base station sector, the orientation of the antennas in tilt and azimuth, the transmit power of base stations/sectors. This is conventionally done by evaluating the physical metrics of ‘coverage’ and ‘capacity’.
Also, non-physical metrics can be used for deployment or optimization of the radio access network, for example balancing of the dissipated traffic per sector, the predicted traffic volume per sector, the maximum predictable cost and/or revenue, and the like.
While radio access network planning provides a first functional configuration, optimization of a radio access network defines an optimum configuration. Optimization of a radio access network configuration aims at enhancing the metrics mentioned above in the concerned area and/or especially for areas of high traffic density. A typical optimization task is also the selection of an optimum position for a base station from a set of candidate positions, known as site selection, or the integration of base stations into an existing network of base stations which is known as site integration. A radio access network, here, is to be understood as the radio network relevant part of a cellular radio network.
The physical metric ‘coverage’, is commonly expressed as a logarithmic measure of the receive field strength of pilot or reference signals of the respective mobile radio technology which is to be planned or optimized. The receive field strength is calculated from the transmit power of the transmitters and the attenuation of the radio channel between transmitter and receiver positions which is referred to as ‘path attenuation’, gains of transmit and receive antennas, and optionally from further gains or losses in the hardware of the transmitters and receivers.
The physical metric ‘capacity’ of a cellular radio technology which does not employ multi-antenna types, is commonly expressed as a logarithmic measure of the signal-to-noise-and-interference ratio (SINR) of pilot or reference signals of the respective mobile radio technology which is to be planned or optimized, or an equivalent metric. This is based on the theoretical channel capacity of a channel with a single input and a single output and exhibiting additive white Gaussian noise, described Claude Elwood Shannon: “A Mathematical Theory of Communication”, Bell System Technical Journal, vol. 27, p. 379-423, and 623-656, July and October 1948) and known as Shannon capacity: CShannon=Id(1+SINR).
In planning and optimizing the configuration of radio access networks it is known to neglect effects arising from so called fast fading. Hence, a channel of a cellular radio technology which do not support multi-antenna installations at the transmitter and/or receiver can generally be described by the scalar value of ‘path loss’. Thus, the physical metrics ‘coverage’ and ‘capacity’ have so far been calculated from path loss which is determined by means of a channel model. There are, e.g. empirical, statistical, and deterministic channel models.
A minimum request on a channel model is to provide a description of the channel which gives the path loss between the transmitter position and the receiver position as a result. Based on such a radio channel model, a conventional radio access network planning/optimization algorithm typically determines for each transmitter-receiver-combination:
the receive field strength per receiver pixel for an antenna configuration, i.e. one resulting value per receiver pixel and per transmitter; and
the SINR per receiver pixel, i.e. one resulting value per receiver pixel and per transmitter.
Necessary information to be input into the radio channel model for planning/optimizing are:
The configuration of a radio access network with multiple base stations, including the positions of the base stations in three-dimensional (3D) space. These base stations might currently be disabled in the radio access network, to form candidate base stations. Such a configuration can be directly imported from (operative) data bases of a network operator.
the transmit power of each of the base stations;
models of the directional characteristic of the antennas used at transmitter and receiver;
the carrier frequency; and
the position of the receivers, which is usually provided as a grid matrix with a fixed resolution (of e.g. 10 m×10 m, or 25 m×25 m), with one receiver per pixel
Advantageous additional input information for planning and optimizing radio access networks are, but not exclusively:                a (typical) grid matrix with a fixed resolution (of e.g. 10 m×10 m, or 25 m×25 m), comprising, per pixel, classified information about the type of the environment and the buildings, including values of physical characteristics (e.g. additional attenuation as a logarithmic value, average building height of the buildings, in meters, optionally other values) (clutter matrix, height clutter matrix). The characteristics per pixel are usually not attributed individually but classified into a limited number of so called clutter classes;        a DEM (Digital Elevation Matrix), DTM (Digital Terrain Matrix), representing a geographical profile of the area;        a three dimensional (3D) vector data model of the buildings;        traffic matrices, i.e. user distribution, in form of a grid matrix with a fixed resolution (of e.g. 10 m×10 m, or 25 m×25 m) and a number of users or user density per pixel;                    economical and technical targets and rules defined by the radio network operator for planning and optimization of the radio access network, such as e.g. a minimum receive field strength, a minimum SINR, a minimum gain through multi-antenna technologies, and the like.                        
New mobile radio technologies such as WiMAX, HSPA+, or LTE inherently support deployment of multi-antenna types at the transmitter and at the receiver, or require such multi-antenna types as a requisite component of the configuration.
A scenario with deployment of multi-antennas both at the transmitter and at the receiver is commonly referred to as a multi-antenna radio channel, or MIMO (multiple input-multiple output).
A radio channel of these recent mobile radio technologies has generally to be modeled as a multi-antenna radio channel. To support installations of multi-antennas at the transmitter and at the receiver when planning and optimizing radio access networks, and to correctly account for the characteristics and values of the eigenvalues of the multi-antennas-channel matrix when approximating the channel capacity, the model of the radio channel has to be multi-antenna compatible.
The channel capacity of a MIMO channel exhibiting additive white Gaussian noise and without channel knowledge at the transmitter is both a function of the signal-to-noise ratio and of the eigenvalues of the channel matrix, see G. J. Foschini and M. J. Gans, “On Limits of Wireless Communications in a Fading Environment When Using Multiple Antennas”, Wireless Personal Communications. Vol. 6, No. 3, March 1998, p. 311-335. According to Foschini, the capacity of a MIMO channel is expressed as
      C          MIMO      ,      Foschini        =            ld      ⁡              (                  det          ⁡                      (                                          I                _                            +                                                SINR                                      n                    t                                                  ⁢                                                      HH                    _                                    *                                                      )                          )              .  where H is the channel impulse response matrix of the MIMO channel.
Therefore, in the MIMO case it no longer suffices to use the SINR for approximating the capacity of a MIMO channel. Rather, the capacity of a MIMO channel is a function of both SINR and the eigenvalues of the channel impulse response matrix.
Depending on the characteristics of the eigenvalues of the multi-antennas channel matrix, such as correlation characteristics, linear independency, a MIMO capacity increase can be achieved by three effects: an increase of the Signal-to-Interference-and-Noise Ratio (SINR) through beam forming, a diversity gain, or spatial multiplexing/cross-polarization multiplexing. As a further challenge, a variety of MIMO antenna types are available including cross-polarized antennas, uniform linear arrays featuring beam forming, and remote radio heads. In terms of MIMO network capacity these antenna types take advantage of different even conflicting radio wave propagation effects.
Thus, in order to select the optimal MIMO antenna type per sector, the MIMO performance of different antenna types in the spatial environment of the sector's position should be analyzed in addition to the classical inspections of the physical metrics coverage and capacity in cellular MIMO network planning and optimization.
In prior art algorithms, the advantages arising from diversity, beam forming and multiplexing, are modeled independently from the actual planning and optimizing algorithm by determining logarithmic metrics (e.g. SINR offsets, presence of multiplexing, and the like) which indirectly represent the characteristics of the eigenvalues. These indirect metrics are then configured, as an input value for the actual planning and optimizing algorithm per clutter class. Shortcomings of this are: The indirect metrics are estimated independently from the actual planning and optimizing algorithm and not calculated by the algorithm itself; and a user of the planning/optimization algorithm has to perform the configuration which is prone to errors, and has to select one advantage per clutter class (not per pixel). This is in sharp contradiction to physical reality according to which the presence and degree of the advantages ‘diversity’ and ‘multiplexing’, in particular with multi-antennas at the transmitter and receiver, is a function of the real building density of the environment of transmitter and receiver, and not of clutter classes.
In case a statistical or empirical channel model for estimation of the indirect metrics, in particular in view of multiplexing obtained through deployment of multi-antenna types in radio access networks is used without an exact model of the building density of the environment of transmitter and receiver, its informational outcome for planning and optimization of the configuration of a radio access network is generally questionable, for physical reasons.
What is lacking in the art is a method which allows for direct modeling of the advantages produced by a deployment of multiple antennas at the transmitter and/or receiver instead of a conventional single antenna, in terms of coverage and capacity.
An object of the invention therefore is to provide an estimation of the advantages resulting from a deployment of multi-antennas in radio access networks in terms of coverage and capacity in a self-contained and algorithmically advantageous way. A more specific object of the invention is to provide a method for analyzing the relative performance of different MIMO antenna types in a potential deployment area and for selecting an optimal MIMO antenna type for a particular coverage sector.